ML Middle/Senior Engineer (Trading)
We are looking for a Machine Learning Researcher to design, develop, and evaluate predictive models for financial markets. You will work at the intersection of quantitative research, machine learning, and real-world trading constraints, contributing to alpha generation and risk modeling.
Requirements:
3+ years of relevant experience
Strong Python skills and experience with ML ecosystems (AWS Sagemaker, MLFlow)
Hands-on experience working with tabular/time series data with usage of ML
Solid understanding of machine learning fundamentals: Supervised learning, feature engineering, model evaluation; Overfitting, regularization, cross-validation
Knowledge of statistical methods and probability theory
Experience with experiment design and offline evaluation
Ability to work with large datasets and build efficient data processing pipelines
Familiarity with SQL and data querying
Strong analytical and problem-solving mindset
Ability to clearly communicate findings and trade-offs
Ownership of tasks from research to implementation
Curiosity and willingness to explore new approaches
Level of English enough for efficient technical and business communication with native speakers
Nice to have:
Experience in financial machine learning, quantitative finance, or trading systems
knowledge of signal generation, alpha research, portfolio construction or risk modeling
Experience with: Deep learning for tabular/time series data (Transformers, RNNs, etc.); Probabilistic modeling or Bayesian methods
Hands-on experience with production ML systems (MLOps, monitoring, retraining)
Ability to define research direction and identify high-impact opportunities
Decision-making under uncertainty
Ability to translate business problems into ML solutions
Responsibilities:
Develop and validate machine learning models for financial time series and cross-sectional data
Conduct research on alpha signals, feature engineering, and predictive modelling techniques
Design experiments and backtesting frameworks with proper statistical rigor
Work with large-scale structured and unstructured financial datasets
Collaborate with engineering teams to deploy models into production pipelines
Analyze model performance, stability, and robustness under changing market conditions
Improve data pipelines, labeling strategies, and evaluation methodologies
We offer:
Projects for such clients as PayPal, Wargaming, Xerox, Philips, Adidas and Toyota
Competitive compensation that depends on your qualification and skills
Career development system with clear skill qualifications
Flexible working hours aligned to your schedule
Options to work remotely